MCMC Supervision for People Re-identification in Nonoverlapping Cameras

We present a pedestrian tracking system that uses re-identification to monitor nonoverlapping cameras. As tracking, re-identification is an assignment problem, the difficulties being to generate an accurate representation and to prune unlikely pairings. The assignments are realised in two stages. First, a Markovian multi-target trackingby-detection framework which includes identification in the search space is run in the cameras. This generates tracks in the cameras and a first assignment between them thanks to the local identification. This solution is then optimized globally by a network supervisor benefiting from coarse topology knowledge over a sliding window with MCMC sampling. The tracking results obtained on a large ground-truthed dataset demonstrate the effectiveness of the approach.